Good day, gentle readers. It turns out, not surprisingly, that no one entered my little redesign competition, and so I’ve no news to report on that front, sadly. Bravely I shall soldier on, however, perhaps to give it another try when circumstances have changed.
Rather than discussing a specific map this time around, I want to take aim at an entire symbology: Chernoff faces.
You’ve probably run into this sort of multivariate symbology before — using faces to convey data. It’s an intriguing idea. As Herman Chernoff proposed in 1973, we can leverage the power of humans to recognize faces to easily communicate information. The face becomes a gateway for people to see patterns in the data.
When Eugene Turner made the above map, he knew that faces carry emotions. As he said on his website, “It is probably one of the most interesting maps I’ve created because the expressions evoke an emotional association with the data. Some people don’t like that.” A face symbology can give people empathy with the numbers — high unemployment is sad, high urban stresses cause anger. Turner could just as easily have made a multivariate symbol map which used abstract geometric figures rather than faces — say, a cross, with each of the four arms changing length according to the data. The map would convey the same information to the reader, but the emotional content — so much of this map’s power to influence readers — would be lost.
Here’s one problem: if you’re using faces, you’re using emotions, so you’d better be prepared to make emotional statements about your data. Empathy can be a powerful force for the narrative you’re trying to convey, but it’s also hard to escape.
This is a student map, from the looks of the website it comes from (not from my university, however), and while I try to avoid bringing up the work of students, this one happens to be a good example of this problem of reading faces. According to this map, it’s sad when people over 50 are executed, but it’s pretty happy when people under 40 are. That’s going to rub a lot of people the wrong way, I would think. This map also suffers from an issue that Turner’s map does: the skin color of the faces. In each map, the fewer white people in an area, the darker the face gets, towards a skin color presumably suggestive of African-Americans. But there are plenty of non-white people who don’t have dark skin. It sets up an easy and dangerous racial spectrum that runs between white and black.
Here’s another Chernoff example symbology, one taken from an ESRI conference paper:
The eyebrows are pretty emotionally charged, and are here linked to how many women are in the workforce. Using their symbology, if you have an area where there’s high unemployment and a lot of women working, you get angry-looking faces. On the other hand, if there’s not a lot of women working, and high unemployment, the faces look sad and depressed. Is this at all sensible or appropriate? More tense emotional states seem to be on display the more women there are in the workforce.
There’s another issue here, besides the dangers of conveying unintentional emotional messages, and that is the simple problem of a nonsensical mismatch between the data and the way its being conveyed. Do places with higher crime have denizens with bigger ears? Does divorce make your nose bigger? Look, it’s not always possible, or even advisable, to make strong visual connections between the symbol and the data (or, if we want to draw on my limited knowledge of fancy semiotics terms: to reduce the gap between the sign vehicle and the referent), but faces seem to me to pose a special case. Perhaps it’s the deliberateness of choice — again, the author didn’t go for something abstract or geometrical, they went for a human face. The reader is not expecting something as out-of-the-blue as “their nose gets bigger when there are more divorced people.” This kind of nonsensical connection breaks the very humanity that the symbol is going for. We know people get angry or sad when there’s high unemployment, and we can relate to that, but their hairline doesn’t recede as their income drops. Why use a face, in the first place, then? These sort of mismatches bother me, but I’m still working out an articulate explanation as to why — perhaps you all can help me with that.
One more example:
A place with a lot of young voters has a big fat nose? And your eyes get bigger, I suppose, if you’re likely to be in a service occupation. Again, people might disagree with me on whether or not this is a problem. I think not paying enough attention to the emotional content of a face is a bigger issue than these sorts of lesser mismatches between eye shapes and % service employees. But the deliberateness, the unusualness, of employing a face suggests to me that I should be looking for a connection, and I am frustrated not to find it.
Chernoff faces can let you bring a lot of power to bear on social data, by showing how people feel right on the map. But it’s easy to squander or misuse their great potential by treating them as simply something cute, amusing, or attention-getting. They require some thought to use.
One Nice Thing: I applaud all of these people for giving Chernoff faces a try — they are a challenge to employ, and not just for the reasons I discussed; they’re also time-consuming to actually draw in most cases. And everyone here used them for social data, which seems the right idea to me. Data about people, shown with faces of people. Much better than say, geological data, which Chernoff used as one of his initial examples.